Selecting content objects for recommendation based on content object collections

ABSTRACT

Each image of a plurality of user-defined collections of images has properties associated therewith. A seed image is defined as currently viewed or selected image and the properties associated with the seed image are retrieved. The seed image is used to identify seed collections as the collections of images which have the seed image as one of their images. A plurality of candidate images are identified from the seed collections. For each candidate image, a significance score is determined as a function of either the number of seed collections to which the candidate image belongs or the ratio of the number of seed collections to the set of all collections to which the candidate image belongs. Each candidate image is ranked based on the significance scores and the seed image properties. At least one candidate image is recommended to the user based on the ranking of the candidate images.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/376,666, filed Dec. 13, 2016, now U.S. patent Ser. No. ______, whichis a continuation of U.S. application Ser. No. 13/791,628, filed Mar. 8,2013, now U.S. Pat. No. 9,535,996, which claims the benefit of U.S.Provisional Application No. 61/695,010, filed Aug. 30, 2012, all ofwhich are incorporated by reference in their entirety.

FIELD OF ART

The present disclosure generally relates to the field of contentobjects, and more specifically to the ranking of content objects forrecommendation to a user based on collections of content objects.

BACKGROUND

The Internet has become a medium for the display and playback of variousforms of content objects. As used herein, “content object” can refer todigital text (such as a document, a poem, a book, an article, aspreadsheet, and the like), a digital image (such as a digital versionof a photograph, a painting, a drawing, a computer-made image, and thelike), digital video (such as a digital feature film, a home-video orother amateur-captured video, a music video), a digital sound recording(such as a digital song, a speech, or any other audio clip), a digitalanimation (such as a .GIF animation, a cartoon, still-motion video, andthe like), or any other form of content that is displayed, viewed, orplayed on a user device over the internet.

Content hosting services on the Internet allow users to access contenton the content hosting services for display and playback. Contenthosting services can allow users to upload content objects to thecontent hosting services. The content objects uploaded by users may beuser-generated content objects, such as images of real-world physicalartwork created by the user or images including digital artwork createdby the user. Content hosting services can also retrieve content objectsfrom other entities, such as professional movie studios, professionalmusic studios, libraries, and the like.

Once content is stored at a content hosting service, users can browseand view or play the content. In order to aid a user in viewing contentof interest to the user, a content hosting service can recommend variouscontent to the user. Selecting relevant content to recommend to a useris very challenging, particularly in the field of digital art, as auser's visual tastes can vary in a difficult-to-predict way.

SUMMARY

There is therefore provided, in accordance with a preferred embodimentof the present invention, a method of selecting an image forrecommendation to a user of a content hosting service. The methodoperates on the content hosting service, which has a content storagemodule, a collection storage module and a processor and is operativewith a client module. The method includes storing a plurality ofuser-defined collections of images in the collection storage module froma plurality of the users of the content hosting service, where eachimage has properties associated therewith. A seed image is defined as animage currently viewed or selected by a current user and the processorretrieves the properties associated with the seed image. The seed imageis used to identify seed collections from among the user-definedcollections of images which have the seed image as one of the images ofthe seed collection. The plurality of candidate images are identifiedfor recommendation from the selected seed collections. For eachcandidate image, a significance score is determined as a function ofeither the number of seed collections to which the candidate imagebelongs or the ratio of the number of seed collections to the set of allcollections to which the candidate image belongs. Each candidate imageis ranked based on the significance scores and on the seed imageproperties and at least one candidate image is recommended to the uservia the client module based on the ranking of the candidate images.

There is alternatively therefore provided, in accordance with apreferred embodiment of the present invention, a method of selecting animage for recommendation to a user of a content hosting service. Themethod operates on the content hosting service, which has a contentstorage module, a collection storage module and a processor and isoperative with a client module. The method includes storing a pluralityof user-defined collections of images in the collection storage modulefrom a plurality of the users of the content hosting service, where eachimage has properties associated therewith. A set of seed images isdefined as an image currently viewed or selected by a current user andthe processor retrieves the per-seed-image properties associated withthe set of seed images. The set of seed images is used to identify seedcollections from among the user-defined collections of images which haveat least one of the seed images as one of the images of the selectedseed collections. The plurality of candidate images are identified forrecommendation from the selected seed collections. For each candidateimage, a significance score is determined as a function of either thenumber of seed collections to which the candidate image belongs or theratio of the number of seed collections to the set of all collections towhich the candidate image belongs. Each candidate image is ranked basedon the significance scores and on the per-seed-image properties and atleast one candidate image is recommended to the user via the clientmodule based on the ranking of the candidate images.

Moreover, in accordance with a preferred embodiment of the presentinvention, the seed image properties include a category of the seedimage or characteristics of the seed image.

Further, in accordance with a preferred embodiment of the presentinvention, characteristics of the seed image the seed image propertiesinclude tags associated with the seed image.

Still further, in accordance with a preferred embodiment of the presentinvention, characteristics of the seed image the method includesretrieving extrinsic properties of the seed image to use in the ranking.

There is also provided, in accordance with a preferred embodiment of thepresent invention, a content hosting system including a processor andoperative with a client module. The system includes a collection storagemodule and a content recommendation engine. The collection storagemodule stores a plurality of user-defined collections of images from aplurality of the users of the content hosting service, each the imagehaving properties associated therewith. The content recommendationengine is operative on the processor to select an image forrecommendation to a user of the content hosting service. The engineincludes a seed object module, a candidate identification module and aranker. The seed object module defines a seed image as an imagecurrently viewed or selected by a current user, retrieves the propertiesassociated with the seed image and uses the seed image to identify seedcollections from among the user-defined collections of images which havethe seed image as one of the images of the seed collection. Thecandidate identification module identifies a plurality of candidateimages for recommendation from the selected seed collections anddetermines for each candidate image, a significance score as a functionof either the number of seed collections to which the candidate imagebelongs or the ratio of the number of seed collections to the set of allcollections to which the candidate image belongs. A ranker ranks eachcandidate image based on the significance scores and on the seed imageproperties and recommends at least one candidate image forrecommendation to the user via the client module based on the ranking ofthe candidate images.

Moreover, in accordance with a preferred embodiment of the presentinvention, the seed image properties include a category of the seedimage or characteristics of the seed image.

Further, in accordance with a preferred embodiment of the presentinvention, characteristics of the seed image the seed image propertiesinclude tags associated with the seed image.

Still further, in accordance with a preferred embodiment of the presentinvention, the ranker further to retrieve extrinsic properties of theseed image to use in the ranking.

The features and advantages described in the specification are not allinclusive and, in particular, many additional features and advantageswill be apparent to one of ordinary skill in the art in view of thedrawings, specification, and claims. Moreover, it should be noted thatthe language used in the specification has been principally selected forreadability and instructional purposes, and may not have been selectedto delineate or circumscribe the inventive subject matter.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a content hosting service environmentconfigured to make content object recommendations to a user, accordingto one embodiment.

FIG. 2 illustrates the various components of a content recommendationengine, according to one embodiment.

FIG. 3 illustrates a simple example embodiment of the organization ofcontent objects in a corpus into collections, according to oneembodiment.

FIG. 4 is a flowchart of a process for selecting a content object forrecommendation, according to one embodiment.

FIG. 5 is a flowchart of a process for ranking candidate content objectsfor recommendation based on the centrality of a seed object to eachcollection and the coherence of each seed collection, according to oneembodiment.

The figures depict embodiments for purposes of illustration only. Oneskilled in the art will readily recognize from the following descriptionthat alternative embodiments of the structures and methods illustratedherein is employed without departing from the principles of theinvention described herein.

DETAILED DESCRIPTION Overview

FIG. 1 is a block diagram of a content hosting service environmentconfigured to make content object recommendations to a user, accordingto one embodiment. The embodiment of FIG. 1 includes a content hostingservice 100, a content provider 120, and a client 130, communicativelycoupled through the network 140. The content provider 120 provides oneor more content objects (collectively referred to herein as “content” or“objects”) to the content hosting service 100. The content is of anydigital form or format capable of display or playback at the client 130to a user. It should be noted that although only one content provider120 and one client 130 are displayed in the embodiment of FIG. 1, inpractice, other embodiments can have any number of either entity, forinstance hundreds of content providers and millions of clients.

Content objects can come in a variety of formats. For instance,text-based content objects may be any format capable of displaying textto a user, such as the .doc format, the .pdf format, the .txt format,any of the variety of e-book formats, .xls, or an image of text.Image-based content objects may be any format capable of displaying animage to a user, such as the .jpeg format, the .tiff format, the .pngformat, the .gif format, and the .bmp format. Video-based andanimation-based content objects may be any format capable of beingplayed to a user, such as the family of MPEG formats, the .avi format,the .mov format, the .mp4 format, the .mkv format, and the .wmv format.Sound-based and audio-based content objects may be any format capable ofbeing played to a user, such as the .mp3 format and the .wav format.Additional content objects can include games and applications (forinstance, computer applications, mobile applications, or web-basedapplications).

The content provider 120 and the client 130 may be any device or servicecapable of transmitting and/or receiving content from the contenthosting service 100 over the network 140. In one embodiment, the contentprovider 120 and the client 130 are computing devices that executeclient software, e.g., a web browser or built-in client application, toconnect to, upload content to, and receive content from the contenthosting service 100 via a network 140, and to display or playbackcontent. The content provider 120 and the client 130 might be, forexample, a personal computer, a personal digital assistant, a mobilephone or smart phone, a tablet, a laptop computer, an internet-enabledtelevision or set-top receiver, and the like. The network 140 istypically the Internet, but may be any network, including but notlimited to a LAN, a MAN, a WAN, a mobile wired or wireless network, aprivate network, or a virtual private network.

Generally, the content provider 120 provides content objects to thecontent hosting service 100 and the client 130 displays or plays theprovided content objects. In practice, the content provider 120 and theclient 130 may be the same device, for instance when the content objectprovided is created by the user 135. Additionally, the content provider120 may be the same entity that operates the content hosting service100. In one embodiment, the content hosting service 100 exposes anapplication programming interface that enables the content provider 120to perform various functions at the content hosting service 100. Contentprovider functions can include, for example, uploading a content objectto the content hosting service 100, editing a content object stored bythe content hosting service 100, and identifying various properties ofcontent stored by the content hosting service 100, such as the author orcreator of the content, the category of the content, the date of thecreation of the content, the identity of any individuals associated withthe creation of the content (for instance, the actors in video content,the musical artist in song content, etc.), commentary or quality ratingof the content, or any other property associated with the content.

Similarly, the content hosting service 100 exposes an applicationprogramming interface that enable a client device to perform variousclient functions at the content hosting service 100. Client functionsinclude enabling a user to establish an account at the content hostingservice 100, to browse content objects at the content hosting service100, to identify various properties of content stored by the contenthosting service 100 (as discussed above), and to make purchasesassociated with the content objects stored by the content hostingservice 100 (such as physical prints of content object images).

One such client function that a user of a content provider or a clientdevice can perform is organizing content objects stored at the contenthosting service 100 into collections. As used herein, the one or morecontent objects organized into a collection are referred to as “members”of the collection. A collection of content objects is a set ofassociated content objects stored at the content hosting service 100.The set of content objects in a collection may be selected by a user.For example, a user can assemble a collection of content objects, andeach of the content objects in the collection can have one or moreproperties in common. A collection can include content objects selectedbased on the type of content object (for instance, videos, oilpaintings, sculptures, photographs, etc.), based on the subject matteror theme of the content object (e.g., dogs, baseball, flowers), based onthe author or creator of the content object, or based on any otherproperty of content objects identifiable by meta-data stored inconjunction with the content objects. Collections can also includecontent objects selected based on subjective properties, for instanceproperties reflective of a user's taste in content objects, and thus arenot limited to groups of content objects with common objectiveproperties. It should be noted that a user can create a collectionwithout explicitly intending to create a collection. For example, acollection may be automatically created based on objects that a userviews during a viewing session, during a period of time, and the like. Acollection can also be created automatically. For example, withoutexplicit user input, the collection creation module 106 canautomatically group content objects into collections based on the typeof content object, the category of the object, the author or creator ofthe content object, the inherent data of the content object (such as itsvisual appearance, sound, or text), recorded user behavior relating tothe content, or based on any other property of content objectsidentifiable by meta-data stored in conjunction with the contentobjects.

The content hosting service 100 represents a system such as that ofDEVIANTART™ that stores and provides content objects such as images tousers via clients such as the user via the client 130. The contenthosting service 100 communicates with content providers and clients viathe network 140 to facilitate the receiving of and displaying/playbackof content objects between entities. The content hosting service 100 maybe implemented in a cloud computing network, accessible by the contentprovider 120 and the client 130 over the network 140. The contenthosting service 100 is configured to make content object recommendationsto a user based on, for example, the content object being viewed by theuser and the properties of the content object, and collections ofcontent objects organized by other users and the properties of thecontent objects in the collections. It should be noted that while thedescription herein may focus on image-based content objects for thepurposes of simplicity, the principles discussed herein are equallyapplicable to all forms and formats of content objects.

The content hosting service 100 includes a front-end interface 102, acontent upload/serving module 104, a collection creation module 106, acontent recommendation engine 108, a content storage module 110, and acollection storage module 112. Other conventional features, such asfirewalls, load balancers, authentication servers, application servers,failover servers, site management tools, and so forth, are not shown soas to more clearly illustrate the features of the content hostingservice 100. While an example of a suitable content hosting service 100is the DEVIANTART website, found at www.deviantart.com, other contenthosting sites can be adapted to operate according to the teachingsdisclosed herein. The content hosting service 100 may display or playcontent objects to a user via a content hosting service interface, suchas a GUI associated with a website or service. The illustratedcomponents of the content hosting service 100 can be implemented assingle or multiple components of software or hardware. In general,functions described in one embodiment as being performed by onecomponent can also be performed by other components in otherembodiments, or by a combination of components. Furthermore, functionsdescribed in one embodiment as being performed by components of thecontent hosting service 100 can also be performed by a content provider120 in other embodiments, if appropriate. It should be furtherappreciated that the various functions described herein for the contenthosting service 100, and in particular those functions relating to theselection, identification, and recommendation of content objects andcollections, are sufficiently complex as to require their implementationin a computer system, and cannot be performed by mental steps.

The front-end interface 102 provides the interface between the variouscomponents of the media hosting service 100 and the content provider 120and client 130. The content upload/serving module 104 receives contentobjects from the content provider 120 and stores the content objects inthe content storage module 110, for instance, at the request of thecontent provider. The content upload/serving module 104 can storemeta-data and other data describing the properties of received contentobjects with the content objects in the content storage module 110. Eachcontent object stored in the content storage module 110 can include orbe associated with a unique object identifier. The contentupload/serving module 104 also retrieves content objects stored at thecontent storage module 110 and transmits the retrieved content objectsto the client 130, for instance, at the request of the client 130.Although not illustrated the embodiment of FIG. 1, the content hostingservice 100 may include additional components allowing a user of aclient 130 to perform various client functions as described above, forinstance, searching or content objects stored at the content hostingservice 100 such as by entering textual queries containing keywords ofinterest, or browsing content objects stored at the content hostingservice 100 such as by filtering content objects by content object type,creator, category, and the like.

The collection creation module 106 allows a user to create a collectionof content objects for storage by the content hosting service 100.Created collections are stored in the collection storage module 112,which includes a data repository such as a relational database,key-value database, or other database management implementations.Collections may be stored by, for example, storing a unique collectionidentifier in conjunction with a unique object identifier for eachcontent object in the collection. It should be noted that any givencontent object can be a member of multiple different collections createdby multiple different users. In some embodiments, a collection (a“parent collection”) can also include another collection (a “childcollection”) as a member. In these embodiments, all content objectsbelonging to the child collection are included as members of the parentcollection.

Stored collections are associated with an identifier for a particularuser or a user account that created the collection, along withinformation entered by the user describing the collection (e.g., textualdescription, keywords, labels, etc.), a designation as to whether thecollection is public or private, or any other information related to thecollection or the content objects in the collection. The content hostingservice 100 is configured to allow a user to browse a stored collection,for instance, by displaying all of the content objects belonging to thecollection. The content storage module 110 stores indices for the storedcontent objects, and the collection storage module 112 stores indicesfor the collections. The stored indices allow objects and collections tobe searched by their author, uploader, keyword, subject matter, themes,content types, or any of the properties of the content objects andcollections. Thus, the front-end interface 102 is configured to receivefrom a client 130 a search query, such as keywords, content orcollection properties, and pass that query to the content storage module110 and the collection storage module 112, which returns resultsidentifying stored content objects and stored collections relevant tothe performed searches. For example, a user can search “landscapewatercolors,” and the content hosting service 100 can return a mix ofcontent objects related to landscape watercolors and collections relatedto or containing landscape watercolor art. It should be noted that thecontent objects stored in the content storage module 110 may be indexedby collections to which each content object is a member, allowing a userto query the content hosting service 100 using a particular contentobject to identify collections to which a content object is a member.

The content recommendation engine 108 is configured to recommend contentobjects to a user, and is described in greater detail below with regardsto FIG. 2. The content recommendation engine 108 is configured toidentify one or more candidate content objects to recommend to a user,and is configured to rank and select one or more of the candidatecontent objects for recommendation. “Recommending” a content object caninclude displaying the content object to the user via a content hostingservice interface displayed on the client 130. For example, a user canview a particular image stored at the content hosting service 100 in aninterface on a website associated with the content hosting service 100,and the content hosting service 100 can display one or more recommendedcontent objects in a different part of the interface, for instance belowthe particular image, in an interface margin, or in a dedicated“recommended content objects” portion of the interface.

Identifying Candidate Objects

FIG. 2 illustrates the various components of a content recommendationengine, according to one embodiment. The content recommendation engine108 includes a seed object module 200, a candidate identification module202, a ranking module 204, a coherence module 206, a centrality module208, and a selection module 210. In other embodiments, the contentrecommendation engine 108 can include fewer, additional, or differentmodules, the functionalities of which may be distributed or performeddifferently than described herein.

When a user of the client device is viewing an individual contentobject, either from a search result, browsing, or other access, thefront-end interface 102 passes the object identifier for the contentobject to the seed object module 200. Given the content objectidentifier, the seed object module 200 retrieves content object propertyinformation associated with the seed object, such as meta-data stored inconjunction with the seed object, the categorical organization of theseed object, the creator or uploader of the seed object, thecharacteristics of the seed object (whether raw characteristics, such asthe resolution, the dimensions, the file size, the run length, and thelike; or processed characteristics, such as a color or shapedistribution, image area, aural frequencies, word frequencies, and thelike), user-created or computer-created tags for the seed object, or anyother information related to the seed object (referred to herein as the“properties” of the seed object). It should be noted that althoughreference is made herein to a single seed object, the principlesdescribed herein apply equally to a set of seed objects.

The candidate identification module 202 identifies a set of candidatecontent objects (referred to herein as “candidate objects”) forrecommendation to a user based on the seed object. To identify candidateobjects, the candidate identification module 202 identifies one or morecollections of which the seed object 200 is a member (referred to hereinas “seed collections”). In one embodiment, identifying seed collectionsincludes querying the collection storage module 112 of the embodiment ofFIG. 1 with the object identifier of the seed object and retrieving thecollection identifier of each collection that includes the seed object.

Once the candidate identification module 202 identifies the one or moreseed collections, the candidate identification module 202 thenidentifies content objects (apart from the seed object) that are membersof the seed collections. In one embodiment, identifying the contentobjects in the seed collections includes querying the collection storagemodule 112 using the collection identifier associated with each seedcollection, and obtaining a set of object identifiers for the contentobjects that are members of each seed collection. Thus, the union of theset of identified content objects that are members of the seedcollections makes up a set of candidate objects representing potentialcontent objects for use by the content recommendation engine 108 inrecommending a content object to a user.

FIG. 3 illustrates a simple example embodiment of the organization ofcontent objects in a corpus into collections, according to oneembodiment. FIG. 3 includes a corpus 310 of content objects, forinstance content objects stored by the content hosting service 100 ofFIG. 1. The corpus 310 includes a plurality of content objects such asthe content object 300 (referred to as the “seed object”), the contentobjects 320, 322, 324, 326, 328, 330, 332, 334, 336, 352, and others notdisplayed in the embodiment of FIG. 3. Some of the content objects ofthe corpus 300 are organized into collections (for instance by machineor by users of the content hosting service 100 of FIG. 1). In theembodiment of FIG. 3, the collection 340 includes the content objects300, 320, 322, and 324; the collection 342 includes the content objects300, 326, 328, 330, and 332; and the collection 344 includes the contentobjects 300, 332, 334, and 336. In the embodiment of FIG. 3, the contentobject 352, and all other content objects either belong to collectionsnot displayed in FIG. 3, or may not belong to collections.

In the context of the embodiment of FIG. 3, the seed object module 200of FIG. 2 receives the identifier of the seed object 300 as a contentobject being displayed, played, or interacted with by a user. Thecandidate identification module 202 then identifies the seed collectionsthat include the seed object 300 as a member, namely collection 340,collection 342, and collection 344. The candidate identification module202 then identifies the other content objects in these seed collectionsas candidate objects, namely the objects 320, 322, 324, 326, 328, 330,332, 334, and 336. Note that as the objects 352 and other objects in thecorpus 310 are not members of a seed collection, such objects are notincluded in the set of identified candidate objects.

Ranking Candidate Objects

The ranking module 204 produces a ranking of the candidate objectsidentified by the candidate identification module 204 based on a varietyof factors, the relevance of each candidate object to a user and a seedobject, and accordingly based on the suitability of each candidateobject to be recommended by the content recommendation engine 108. Theranking module 204 ranks the candidate objects based on a number ofdifferent criteria, such as the various measures of significance of thecandidate object relative to the seed collections; the properties of thecandidate objects, the seed object, the seed collections, or a viewinguser; the coherence or entropy of the seed collections; the centralityof the seed object to the seed collections; or a combination of thesecriteria.

1. Ranking Based on the Significance of the Candidate Object Relative tothe Seed Collections

The ranking module 204 can use various measures of the significance ofthe candidate object relative to the seed collections to rank theobject. One such measure is the number of seed collections to which acandidate object belongs. For example, the rank of a candidate objectcan be proportional to the number of seed collections to which theobject is a member. Thus, the larger the number of seed collections towhich a candidate object belongs, the higher the rank of the candidateobject.

Another measure of significance is the importance of a candidate objectto the seed collections to which the candidate object belongs relativeto all collections at the content hosting service 100. In other words, acandidate object may be ranked based on the ratio of the number of seedcollections to which the candidate object belongs to the number of allcollections to which the candidate objects belong. In one embodiment,such a ranking is determined using a variant of the termfrequency-inverse document frequency (“TF-IDF”) weighting for eachcandidate object, treating the candidate object as a “term,” and eachcollection as a “document.” Alternatively, other methods of frequencynormalization may be used to determine the ranking of each candidateobject based on the importance of the candidate objects to the seedcollections relative to all collections at the content hosting service100.

2. Ranking Based on Properties of Objects, Collections, and a ViewingUser

The ranking module 204 can rank the candidate objects in additionalways. In one embodiment, the ranking module 204 can rank the candidateobjects based on extrinsic properties of the candidate objects and theseed object. For example, the ranking module 204 can rank the candidateobjects based on a number of views or plays that a candidate objectreceives, based on a determined importance or relevance of the creatoror uploader of the candidate object, or other properties that are notinherent in the object itself. Ranking can also be based on any of theintrinsic properties of the seed object and/or the candidate objects,which are those descriptive of each object and the object's author, suchas a categorization of the object, tags associated with the object, theupload date of the object, and the like. Ranking can also be based onthe extrinsic and intrinsic properties of the seed collections to whicha candidate object belongs, such as a determined relevance of each seedcollection, tags associated with the seed collection, or, in the case ofuser-created collections, the creator of the seed collection. In oneembodiment, the ranking module 204 can rank the candidate objects basedon characteristics of a viewing user of the object. For example, theranking module 204 can rank the candidate objects based on userpreferences (such as preferred content object formats orcategorizations), based on past content object viewing or playinghistory of a user, and the like.

It should be noted that candidate objects may be ranked based on acombination of the significance of the candidate object relative to theseed collections and the properties of the candidate objects, the seedobject, the seed collections, and a viewing user. For example, theranking module 204 can initially rank candidate objects based on thenumber of seed collections in which each candidate object appears. Assuch a ranking method can result in multiple candidate objects sharingthe same rank within the ranking, the ranking module 204 can adjust theranking based on the categorization of the candidate objects using acategory-preference ranking, which pre-ranks content objects categories(e.g., pre ranked categories based popularity of category or number ofobjects in the category). In such an example, the highest-rankedcandidate objects are the candidate objects that belong to the most seedcollections and that belong to the most highly pre-ranked categories.Alternatively, the ranking module 204 can adjust the ranking based onany other property of the candidate objects, the seed object, the seedcollections, or the viewing user.

3. Ranking Based on the Significance of the Candidate Object Relative tothe Seed Collections

The ranking module 204 can rank candidate objects based on the coherenceof the seed collections to which each candidate object belongs. Thecoherence module 206 determines the coherence (or relatedly, theentropy) of each seed collection for use in ranking the candidateobjects by the ranking module 204. As used herein, the coherence of aseed collection is a measure of the similarity of the content objects inthe seed collection to each other, or of the similarity of theproperties of the content objects in the seed collection. Likewise, thecoherence of a seed collection can refer to the distribution of thecontent objects in the seed collection, or of the properties of thecontent objects in the seed collection. Each seed collection can berepresented by one or more collection vectors with entries representingthe properties of the seed collection or of the objects belonging to theseed collection. The collection vectors may collectively be modeled assamples of a multivariate Gaussian distribution over the properties ofthe seed collection or the objects belonging to the seed collection. Insuch an embodiment, the coherence of the seed collection can refer tothe reciprocal of the determinant of the covariance matrix of theGaussian distribution. For example, the coherence module 206 candetermine the coherence of a seed collection made up of images bydetermining the determinant of the covariance of the distribution of thecolors of the images in the seed collection. The coherence of the seedcollection can also refer to other functions of the covariance matrixwhich attempt to summarize the spread of the Gaussian data.

In one embodiment, the coherence module 206 determines the coherence ofa seed collection by first determining a sample covariance matrix of theseed collection. The sample covariance matrix is based on a mean vectorof latent tag vectors for each object in the seed collection. Eachlatent tag vector is the result of a linear transform of the tag vectorof an object in the seed collection. The sample covariance matrix isfurther based on a corpus tag vector covariance matrix and a rawcovariance matrix. The corpus tag vector covariance matrix is determinedas follows:

$\begin{matrix}{S_{ij} = \frac{\sum_{y\; \epsilon \; Y}{{w(y)}{M_{ij}(y)}}}{\sum_{y\; \epsilon \; Y}{w(y)}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In equation 1, y refers to a collection in a set of object collectionsY, w(y) refers to a weight given to collection y, i and j are matrixindices, and M_(ij) (y) refers to a sum of squares matrix of tag vectorelements determined as follows:

$\begin{matrix}{{M_{ij}(y)} = {\frac{1}{{N(y)} - 1}{\sum\limits_{x\; {\epsilon y}}{\left( {{t_{i}(x)} - {{\overset{\_}{t}}_{i}(y)}} \right)\left( {{t_{j}(x)} - {{\overset{\_}{t}}_{j}(y)}} \right)}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In equation 2, N (y) refers to the number of objects in collection y,t_(i)(x) refers to a tag vector for object x, and t _(i)(y) refers to amean tag vector for object collection y. A raw covariance matrix isdetermined as follows:

$\begin{matrix}{{{\overset{\sim}{S}}_{ij}(y)} = {\frac{1}{{N(y)} - 1}{\sum\limits_{x\; \epsilon \; y}{\left( {{l_{i}(x)} - {{\overset{\_}{l}}_{i}(y)}} \right)\left( {{l_{j}(x)} - {{\overset{\_}{l}}_{j}(y)}} \right)}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In equation 3, l_(i)(x) refers to a latent tag vector for an object x inthe seed collection y, and l _(i)(y) refers to a mean vector of latenttag vectors for each object x in the seed collection y. The samplecovariance matrix is determined as follows:

$\begin{matrix}{{{\overset{\_}{S}}_{ij}(y)} = {{\frac{k}{{N(y)} + k}{{\overset{\sim}{S}}_{ij}(y)}} + {\frac{\alpha \; k}{{N(y)} + k}S_{ij}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In equation 4, a and k refer to weights used to adjust the influence of{tilde over (S)}_(ij) (y) and S_(ij). The Gaussian coherence of a seedcollection y is determined as follows:

$\begin{matrix}{{{Coherence}(y)} = \frac{1}{\det \left( {{\overset{\sim}{S}}_{ij}(y)} \right)}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

In equation 5, det ({tilde over (S)}_(ij) (y)) refers to the determinantof the sample covariance matrix.

Alternatively, the coherence of a seed collection can be measured by adecreasing function of the entropy of the properties of the contentobjects in the seed collection. It should be noted that the distributionover which entropy is derived is not limited to the Gaussian modeldescribed above, and can for instance include a Dirichlet topic model.In such an embodiment, the coherence module 206 can determine thecoherence of a seed collection by determining a decreasing function ofthe entropy of a distribution of measurements representing theproperties of the objects belonging to the seed collection and the seedcollection itself. For example, the coherence module 206 can determinethe entropy of a seed collection made up of images based on thedistribution of tags associated with the images, and can determine thecoherence of the seed collection with respect to the tags associatedwith the images by taking the reciprocal of the determined entropy. Itshould be noted that the coherence module 206 may determine thecoherence or entropy for each of a number of properties of the contentobjects in the seed collection, and then may average the determinedcoherences or entropies with respect to these properties to determinethe coherence or entropy of the entire seed collection.

In one embodiment, the coherence module 206 determines the entropy of aseed collection as follows:

$\begin{matrix}{{h(y)} = {- {\sum\limits_{c,{{N{({c,y})}} > 0}}{{p\left( {c,y} \right)}\log \; {p\left( {c,y} \right)}}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

In equation 6, c refers to a category, y refers to a collection, N(c, y)refers to the number of content objects in a collection y assigned to acategory c, and p(c, y) is determined as follows:

$\begin{matrix}{{p\left( {c,y} \right)} = \frac{{N\left( {c,y} \right)} + \alpha}{{N(y)} + \beta}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

In equation 7, N (y) refers to the number of objects in collection y,and α and β refer to small positive parameters for tuning p(c, y).

In addition to determining one or more collection vectors, the coherencemodule 206 can determine an object vector for each candidate object in aseed collection, with each object vector entry representing a particularproperty, such as a characteristic, measure, quantity, or valueassociated with the candidate object. The coherence module 206 can thendetermine the coherence or entropy of the seed collection based on theobject vectors associated with the candidate objects in the seedcollection. In the event that the object vectors are of acomputationally-prohibitive dimensionality, the coherence module 206 mayperform PCA/SVD on the object vectors in order to reduce thedimensionality the object vectors prior to determining the coherence orentropy of the seed collection. By performing PCA/SVD, the coherencemodule 206 can reduce or eliminate overlapping information in the objectvectors in order to more accurately or more efficiently determine thecoherence or entropy of the seed collection. It should be noted that inother embodiments, other dimensionality-reduction techniques areimplemented.

The coherence module 206 can determine a coherence score for each seedcollection. Each coherence score directly correlates to the coherence ofthe seed collection (where the coherence score increases as thedetermined coherence of the seed collection increases and vice versa),and inversely correlates to the entropy of the seed collection (wherethe coherence score decreases as the determined entropy increases, andvice versa). It should be noted that any quantification of thedetermined coherence or the determined entropy of a seed collection canbe considered a coherence score; in such instances, a separate and/ordistinct step of determining a coherence score based on a previouslydetermined coherence or entropy is not necessary. The remainder of thedescription will be limited to embodiments where the coherence module206 outputs a coherence score for each seed collection for the purposesof simplicity; in embodiments where a coherence or entropy distributionor other measure is outputted, the principles and methods describedherein are equally applicable.

The ranking module 204 can rank the candidate objects based additionallyon the coherence scores determined by the coherence module 206. In oneembodiment, the ranking module 204 uses the coherence scores for theseed collections as scalar weights in ranking the candidate objects. Inthis embodiment, to determine a rank for each candidate object, theranking module 204 adds the coherence scores together for each seedcollection to which a candidate object belongs, and bases the rank onthe coherence score sum. For example, if a first candidate objectbelonged in two seed collections and if a second candidate objectbelonged in three seed collections, the ranking module 204 can rank thesecond candidate object higher than the first candidate object ifcoherence scores are not taken into account. In this example, if thecoherence scores for the two seed collections to which the firstcandidate object belongs are 0.8 and 0.7, and if the coherence scoresfor the three seed collections to which the second candidate objectbelongs are 0.9, 0.4, and 0.1, the ranking module 204 can rank the firstcandidate object (with a coherence score sum of 1.5) higher than thesecond candidate object (with a coherence score sum of 1.4).

It should be noted that in one embodiment, the determined coherencescores for each seed collection are used as scalar weights for use inadjusting the ranking of the candidate objects by the ranking module204. In this embodiment, the ranking module 204 first ranks allcandidate objects using TF-IDF or another method of frequencynormalization, and then adjusts the rankings based on the sum ofcoherence score scalars for the seed collections to which each candidateobject belongs (for instance by multiplying each candidate object'sTF-IDF weight by the coherence score sum associated with the candidateobject). For example, if the TF-IDF weight and the coherence score sumfor a first candidate object are 1.0 and 1.5 (respectively), and theTF-IDF weight and the coherence score sum for a second candidate objectare 1.2 and 1.0 (respectively), the second candidate object is rankedhigher than the first if the ranking module 204 only considers TF-IDFweightings. However, if the ranking module 204 considers both TF-IDFweightings and coherence scores, the first candidate object (with aTF-IDF and coherence score sum product of 1.5) is ranked higher than thesecond (with a TF-IDF and coherence score sum product of 1.2).

Alternatively, the ranking module 204 can rank the candidate objectsbased on a combination of the coherence scores for the seed collectionsdetermined by the coherence module 206 and based on coherence scores forall other collections to which the candidate objects belong. Thecoherence module 206 can determine a coherence score (or other measureof coherence or entropy) for each collection stored at the contenthosting service 100, for instance when requested by the contentrecommendation engine 108, or in advance. In this embodiment, theranking module 206 can rank candidate objects based on (for eachcandidate object) the quotient resulting from dividing the sum of thecoherence scores for seed collections to which the candidate objectbelongs by the sum of the coherence scores for all collections at thecontent hosting service 100 to which the candidate object belongs.

4. Ranking Based on the Centrality of the Seed Object to Each SeedCollection

The ranking module 204 can rank candidate objects based on therelatedness of the seed object to each seed collection (referred toherein as the “centrality” of the seed object to a seed collection). Thecentrality module 208 determines, for each seed collection, thecentrality of the seed object to the seed collection and produces acentrality score for the seed collection representing the determinedcentrality. Generally, the centrality of the seed object to a seedcollection indicates if the properties of the seed object arerepresentative of the properties of the candidate objects in the seedcollection. In one embodiment, a high centrality score indicates thatthe seed object is representative of the mean of the distribution ofcandidate objects in a seed collection, while a low centrality score canindicate that the seed object is representative of an outlier in thedistribution of candidate objects in the seed collection. It should benoted that although reference is made herein to centrality scores, otherembodiments can implement the methods described herein using othermeasures of centrality and without the explicit determination of acentrality score. In another embodiment, where the seed object itself isrepresented as a distribution of properties, a high centrality scoreindicates a high degree of similarity between the seed object'sdistribution and the distribution of the seed collection. In such anembodiment similarity between distributions can be calculated as theKullback-Leibler divergence of the seed object's distribution withrespect to the seed object's collection's distribution. In otherembodiments, different measures of similarity between distributions canbe used.

The centrality score for a seed collection can indicate the probabilitythat the seed object is coherent to the seed collection. To determinethe centrality score for a seed collection, the centrality module 208can determine a Gaussian distribution of one or more particularproperties of the candidate objects in a seed collection. The centralitymodule 208 can then determine the location in the determineddistribution that represents the values of the one or more particularproperties of the seed object, and can determine a centrality scorebased on this determined location relative to the mean of thedistribution. In such an embodiment, the centrality score of aparticular seed collection is directly correlated to the distance of adetermined location in the distribution representing the seed object tothe mean of the distribution.

The ranking module 204 can rank candidate objects based on thecentrality scores for the seed collections associated with eachcandidate object. In one embodiment, the average centrality score forthe seed collections associated with a candidate object is determinedfor each candidate object, and the candidate objects are ranked based onthe average centrality scores. Alternatively, for each candidate object,a highest centrality score is determined from among the seed collectionsassociated with the candidate object, and the candidate objects areranked based on the determined highest centrality scores.

The ranking module 204 can eliminate candidate objects fromconsideration prior to ranking the candidate objects based on thecentrality scores determined by the centrality module 208. The rankingmodule 204 can remove candidate objects belonging only to seedcollections associated with a centrality score that falls below apre-determined threshold. For example, if the centrality module 208determines that a seed collection has a very low centrality score (belowa particular threshold), the ranking module 204, prior to rankingcandidate objects, can eliminate candidate objects from considerationfor ranking that belong only to the seed collection. The ranking module204 can retain only candidate objects that belong to at least one seedcollection associated with a centrality score that exceeds apre-determined threshold.

Likewise, the ranking module 204 can eliminate candidate objects fromconsideration prior to ranking the candidate objects based on acombination of the centrality scores for the seed collections determinedby the centrality module 208 and the coherence scores for the seedcollections determined by the coherence module 206. In this embodiment,the ranking module 204 can determine, for each seed collection, theproduct of the coherence score and the centrality score associated withthe seed collection. The ranking module 204 can eliminate fromconsideration candidate objects belonging only to seed collections witha determined coherence score and centrality score sum, product, orweighted average that falls below a pre-determined threshold. Theranking module 204 can also only retain candidate objects for rankingconsideration belonging to at least seed collection with a determinedcoherence score and centrality score product that exceeds apre-determined threshold.

In one embodiment, the centrality module 208 can determine a centralityscore for each seed collection prior to the identification of candidateobjects by the candidate identification module 202. In this embodiment,the candidate identification module 202 can select candidate objectsonly from seed collections that have a centrality score above apre-determined threshold. In this embodiment, a content object belongingto a seed collection with a centrality score below a pre-determinedthreshold is selected as a candidate object only if the content objectalso belongs to a seed collection with a centrality score above thepre-determined threshold. A centrality score for a seed collection canalso be combined with a coherence score for the seed collection so thatthe combined centrality/coherence score can be compared to the scores ofother objects in other collections.

Selection of Candidate Objects for Recommendation

The selection module 210 selects one or more of the ranked candidateobjects for display to a user as a recommended content object. Theselection module 210 can select the top ranked candidate object, or atop ranked plurality of candidate objects for display to a user. In oneembodiment, the selection module 210 can select from among a top rankedportion of candidate objects. For example, the selection module 210 canselect the top ranked candidate object that a user has not previouslyviewed or played, or can select the top ranked candidate object in acategory or having a tag in common with the seed object. Any selectioncriteria can be used by the selection module 210 such that one or morecandidate objects are selected based on the ranking of candidate objectsdetermined by the ranking module 204.

It should be noted that the one or more candidate objects may not beexplicitly ranked against each other prior to selection by the selectionmodule 210. In such an embodiment, the ranking module 204 can insteaddetermine a score for each of the candidate objects, or can eliminate aset of candidate objects from consideration for selection by theselection module 210. In this embodiment, the selection module 210 canselect from among the candidate objects randomly, based on theproperties of the candidate objects or the seed objects, based on thecharacteristics of a user, based on the determined coherency of thecandidate objects or centrality of the seed collections, or based on anyother criteria. Once selected, the one or more selected candidateobjects is sent by (for example) the content hosting service 100 of FIG.1 to (for example) the client 130 for display to or playback by a user.

Operation

FIG. 4 is a flowchart of a process for selecting a content object forrecommendation, according to one embodiment. Content object collectionsare stored 400. The collections of content objects may beuser-generated, and may be received from users collectively across auser base. Alternatively, the collections may be machine-generated, andmay be organized into collections based on the properties of the contentobjects.

A request is received 410 to display a seed object. The request isreceived from a user, for instance, through a client device operated bythe user. The user may select the seed object for display via aninterface provided to the client device. In response to receiving therequest, seed collections are identified 420 and candidate objects areidentified 430. Seed collections are identified by querying the storedcollections with a unique identifier for the seed object, andidentifying the stored collections that are associated with theidentifier. The candidate objects can include all content objectsbelonging to the seed collections. The properties of the seed object arealso identified 440. The properties of the seed object can include thecharacteristics of the seed object, whether raw or processedcharacteristics, and the categorization of the seed object, the tagsassociated with the seed object, the authorship of the seed object, andthe like.

The candidate objects are ranked 450 based on the identified seedcollections and the identified seed object properties. The candidateobjects may be ranked based on the number of seed collections to whicheach candidate object belongs, based on the importance of each candidateobject to the seed collections in the context of all stored collections,based on the properties each candidate object has in common with theseed object, and the like. A candidate object is then selected 460 forrecommendation based on the ranking of candidate objects. The top rankedcandidate object may be selected, or one or more candidate objects maybe selected from among a top ranked number of candidate objects.

FIG. 5 is a flowchart of a process for ranking candidate content objectsfor recommendation based on the centrality of a seed object to eachcollection and the coherence of each seed collection, according to oneembodiment. A centrality score is determined 500 for each seedcollection based on the relatedness of the seed object to the seedcollection. The relatedness of the seed object to a seed collection canbe determined by comparing the distribution of properties of candidateobjects in a seed collection to the properties of the seed object, forinstance, by determining the distance from the seed object in thedistribution to the mean of the distribution. Seed collections can beremoved 510 from consideration in ranking candidate objects based on thecentrality scores. For example, identifying candidate objects caninvolve identifying the content objects that belong to seed collectionswith a centrality score above a pre-determined threshold.

The coherence of each seed collection is determined 520 based on theproperties of the candidate objects in each seed collection. Forexample, the coherence of each seed collection can be determined basedon the variance or the entropy of the properties of the candidateobjects in the seed collection. In one embodiment, a coherence score isdetermined for each seed collection. The candidate objects are ranked530 based on the determined coherence of the seed collections. Forexample, the candidate objects associated with seed collections with ahigh coherence score can be ranked higher than the candidate objectsassociated with seed collections with a low coherence score.

The present invention has been described in particular detail withrespect to one possible embodiment. Those of skill in the art willappreciate that the invention may be practiced in other embodiments.First, the particular naming of the components and variables,capitalization of terms, the attributes, data structures, or any otherprogramming or structural aspect is not mandatory or significant, andthe mechanisms that implement the invention or its features may havedifferent names, formats, or protocols. Also, the particular division offunctionality between the various system components described herein ismerely exemplary, and not mandatory; functions performed by a singlesystem component may instead be performed by multiple components, andfunctions performed by multiple components may instead performed by asingle component.

Some portions of above description present the features of the presentinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. These operations, while describedfunctionally or logically, are understood to be implemented by computerprograms. Furthermore, it has also proven convenient at times, to referto these arrangements of operations as modules or by functional names,without loss of generality.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “determine” refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system memories or registersor other such information storage, transmission or display devices.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncould be embodied in software, firmware or hardware, and when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a computer readable storage medium,such as, but is not limited to, any type of disk including floppy disks,optical disks, CD-ROMs, magnetic-optical disks, read-only memories(ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic oroptical cards, application specific integrated circuits (ASICs), or anytype of computer-readable storage medium suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will be apparent to those ofskill in the art, along with equivalent variations. In addition, thepresent invention is not described with reference to any particularprogramming language. It is appreciated that a variety of programminglanguages may be used to implement the teachings of the presentinvention as described herein, and any references to specific languagesare provided for invention of enablement and best mode of the presentinvention.

The present invention is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

Finally, it should be noted that the language used in the specificationhas been principally selected for readability and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention, which is set forth in the following claims.

What is claimed is:
 1. A method of selecting an image for recommendationto a user of a content hosting service, the method operating on thecontent hosting service having a content storage module, a collectionstorage module and a processor and operative with a client module, themethod comprising: storing a plurality of user-defined collections ofimages in said collection storage module from a plurality of said usersof said content hosting service, each said image having propertiesassociated therewith; via said processor, defining a seed image as animage currently viewed or selected by a current user and retrieving saidproperties associated with said seed image; via said processor, usingsaid seed image to identify seed collections from among saiduser-defined collections of images which have said seed image as one ofthe images of said seed collection; via said processor, identifying aplurality of candidate images for recommendation from said selected seedcollections; via said processor, determining for each candidate image, asignificance score as a function of either the number of seedcollections to which said candidate image belongs or the ratio of saidnumber of seed collections to the set of all collections to which saidcandidate image belongs; via said processor, ranking each candidateimage based on said significance scores and on said seed imageproperties; and via said processor, recommending at least one candidateimage for recommendation to the user via said client module based on theranking of the candidate images.
 2. The method of claim 1, wherein saidseed image properties comprise a category of said seed image.
 3. Themethod of claim 1, wherein said seed image properties comprise tagsassociated with said seed image.
 4. The method of claim 1, wherein saidseed image properties comprise characteristics of said seed image. 5.The method of claim 1, and also comprising retrieving extrinsicproperties of said seed image to use in said ranking.
 6. A method ofselecting an image for recommendation to a user of a content hostingservice, the method operating on the content hosting service having acontent storage module, a collection storage module and a processor andoperative with a client module, the method comprising: storing aplurality of user-defined collections of images in said collectionstorage module from a plurality of said users of said content hostingservice, each said image having properties associated therewith; viasaid processor, defining a set of seed image as a set of imagescurrently viewed or selected by a current user and retrieving saidper-seed-image properties associated with said set of seed images; viasaid processor, using said set of seed images to identify seedcollections from among said user-defined collections of images whichhave at least one of said seed images as one of the images of said seedcollection; via said processor, identifying a plurality of candidateimages for recommendation from said selected seed collections; via saidprocessor, determining for each candidate image, a significance score asa function of either the number of seed collections to which saidcandidate image belongs or the ratio of said number of seed collectionsto the set of all collections to which said candidate image belongs; viasaid processor, ranking each candidate image based on said significancescores and on said per-seed-image properties; and via said processor,recommending at least one candidate image for recommendation to the uservia said client module based on the ranking of the candidate images. 7.The method of claim 6, wherein said per-seed-image properties comprise acategory of said seed image.
 8. The method of claim 6, wherein saidper-seed-image properties comprise tags associated with said seed image.9. The method of claim 6, wherein said per-seed-image propertiescomprise characteristics of said seed image.
 10. The method of claim 6,and also comprising retrieving extrinsic properties of said seed imageto use in said ranking.
 11. A content hosting system comprising aprocessor and operative with a client module, the system comprising: acollection storage module to store a plurality of user-definedcollections of images from a plurality of said users of said contenthosting service, each said image having properties associated therewith;and a content recommendation engine operative on said processor toselect an image for recommendation to a user of said content hostingservice, said engine comprising: a seed object module to define a seedimage as an image currently viewed or selected by a current user, toretrieve said properties associated with said seed image and to use saidseed image to identify seed collections from among said user-definedcollections of images which have said seed image as one of the images ofsaid seed collection; a candidate identification module to identify aplurality of candidate images for recommendation from said selected seedcollections and to determine for each candidate image, a significancescore as a function of either the number of seed collections to whichsaid candidate image belongs or the ratio of said number of seedcollections to the set of all collections to which said candidate imagebelongs; a ranker to rank each candidate image based on saidsignificance scores and on said seed image properties and to recommendat least one candidate image for recommendation to the user via saidclient module based on the ranking of the candidate images.
 12. Thesystem of claim 11, wherein said seed image properties comprise acategory of said seed image.
 13. The system of claim 11, wherein saidseed image properties comprise tags associated with said seed image. 14.The system of claim 11, wherein said seed image properties comprisecharacteristics of said seed image.
 15. The system of claim 11, andwherein said ranker further to retrieve extrinsic properties of saidseed image to use in said ranking.